code: def test_predict_pass():
      X = np.array([[1], [2], [3]])
      y = np.array([1, 2, 3])
      est = BlankClassifier(print_progress=0, random_seed=1)
      est.fit(X, y)
      est.predict(X)
  
      with pytest.raises(TypeError) as excinfo:
          est.fit(X)
          assert excinfo.value.message == (
              "fit() missing 1 " "required positional " "argument: 'y'"
          )
  
file_path: mlxtend/mlxtend/_base/tests/test_classifier.py
imports: import numpy
  import pytest
  from mlxtend._base import _BaseModel, _Classifier
label_desc: Use image node_id 8 for calling a global function with example usage: test_predict_pass() without return types